Urban Travel Demand Research Articles

Accuracy of Zonal Socioeconomic Forecasts for Travel Demand Modeling: Retrospective Case Study

Modeling for urban travel demand begins with 20-year forecasts of population, households, vehicle ownership, and employment for a region's individual transportation analysis zones. Yet even though these models rely on socioeconomic forecasts, the long-term accuracy of such models has not received attention, especially for smaller regions with limited planning staff. This paper reports on a case study of the socioeconomic predictions made in 1980 for a horizon year of 2000, by comparing predicted and actual results in Lynchburg, Virginia. The region percentage error reflects the difference between forecast and observed values for the entire region. Although regional forecasts for the number of vehicles and employment showed errors of less than 10%, those forecasts for population and households showed errors of 48% and 14%, respectively. The failure of planned development in two of the region's 68 zones accounted for much of this error, such that removal of these two zones lowered population and household region percentage errors to 10% and 1%, respectively. The zone percentage error is the average of all individual zone percentage errors. Even after removal of the two aforementioned zones, population, households, vehicles, and employment had strikingly large zone percent errors of 39%, 48%, 45%, and 136%, respectively. These results make a compelling case for executing the regional travel demand model twice: once with the given socioeconomic forecasts and once with forecasts modified on the basis of expected errors. For regions that have not conducted an assessment such as that presented here, the expected errors from this paper may be used.

Measuring the structural determinants of urban travel demand

To be best prepared for tomorrow's cities we need to forecast urban travel demand. To this end, this study calibrates an urban travel demand model, which uses the principal structural variables that have been identified in the literature. It uses a robust econometric method, which has been little applied in the sphere of transportation. The results show that two variables stand out from the others: the user cost of transport – by private car and public transport – and urban density. It is surprising, but explicable with the available data, that the demand functions estimated for a given country are independent from the group of countries to which it belongs.

Analyzing Competition between the High Speed Train and Alternative Modes. The Case of the Madrid-Zaragoza-Barcelona Corridor

While there have been many studies of urban travel demand, little attention has been paid to the analysis of inter-urban rail travel demand. Studies of inter-urban rail demand usually focus on assessment through the conventional cost benefit analysis of this type of investments, in which the emphasis is on the cost side. However, the analysis of the potential benefits, bearing in mind the intermodal competition, is usually neglected. This paper analyzes the potential competition of the high speed train (HST) with the main competing modes on the Madrid-Barcelona route, where a new HST infrastructure has been recently built. The analysis is based on the estimation of disaggregated Nested Logit models using information provided by travellers in the main corridors: Madrid-Zaragoza and Madrid-Barcelona. The utility specification considers the effect of the main level-of-service attributes as well as some latent variables on modal choice. We analyze demand response to various policy scenarios that consider the potential competition between HST and other modes as well as the willingness to pay for improved levels of service. The results highlight the low level of competition that the HST could exert over air transport services in Madrid-Barcelona corridor, showing that policy makers may have been very optimistic about the figures of traffic diversion from air that could be attained.

Structural Analysis of How Urban Form Impacts Travel Demand: Evidence from Taipei

This study empirically examines the connections between urban form and travel demand at the aggregate level using traffic analysis zone data from Taipei, Taiwan, for the year 2000. Nine latent variables and 26 observed variables were analysed using structural equation modelling. By clarifying the direct and indirect effects, the empirical evidence indicates that density is positively related to trip generation and negatively associated with private mode split; mixed land use reduces trip generation and indirectly increases private mode split and a pedestrian-friendly built environment significantly reduces private mode split. Empirical evidence indicates that land use density, diversity and design affect travel demand in urban areas both directly and indirectly.

IMPACT: An integrated GIS-based model for simulating the consequences of demographic changes and population ageing on transportation

Recent advancements in computing and geographic information systems (GIS) have revolutionized the development of planning support systems (PSSs) to study and simulate the future of travel demand in urban areas. While travel demand models have been widely developed and used to inform the urban planning process, very little has been done to explicitly account for demographic changes and the ageing of population within such models. The work in this paper bridges this gap by describing the development and application of IMPACT (integrated model for population ageing consequences on transportation), a GIS-based PSS capable of assessing the ramifications of demographic changes, including population ageing, on the performance and usage of urban transportation systems. Unlike existing operational PSSs, IMPACT possesses unique and novel features that enable it to model demographic changes and urban travel demand for various age cohorts. Through the simulation of scenarios for varying fertility rates, the paper explores and quantifies the impacts of ageing and population age-structure changes on travel patterns in the census metropolitan area (CMA) of Hamilton in Ontario, Canada. The analysis suggests a consistent increase in the number of trips and mobility levels of elderly drivers, as a result of population ageing over time. Furthermore, increases in fertility rates could significantly impact the usage and performance of the transportation system for certain periods of the day, by the year 2031. Results such as these are important to consider when planning the future of cities. The findings in this paper provide new insights into the future of urban travel in the Canadian context.

Variable Decision Strategies, Rational Choice, and Situation-Related Travel Demand

Classical economic theory following Bentham and Mill assumes decision makers are completely informed, highly sensitive to differences between alternatives, and above all rational in being able to rank order the possible alternatives and choose among them on some measure of welfare (utility maximization). In recent years, this view has been successfully challenged by empirical studies of decision making, and many alternative decision rules have emerged (such as lexicographic, elimination by aspects, counting, conjunctive, heuristic additive difference, affective, take the best, and so on). Despite thirty years’ work on boundedly rational and cognitive approaches, the classical view is still widely accepted in urban travel demand modeling. This paper develops a newer conceptual framework for shopping travel situtations—namely, choice of shopping place on different kinds of tour, for different orders of goods, and for regional malls and neighborhood centers. Hypotheses concerning the use of different kinds of decision rule are generated for the different situations or contexts. A statistical design is provided for tests of these hypotheses using fieldwork, verbal protocol analysis, and low-level t-tests. Fifty-five MIT students are interviewed, and their protocols for the shopping situations recorded and analyzed. The findings are that decision rules are multiple and vary with context. Although the specific hypotheses of the paper are not strongly supported, there is overwhelming evidence of the widespread use of simple heuristics. Implications of the findings are drawn for future work in the travel demand field and the preference and choice field. In addition, the contribution of this paper to behavioral decision making is noted, through the extension of work on choice heuristics to a different field (travel demand) and the use of a fieldwork qualitative methodology.

Study on the Impacts of Urban Density on the Travel Demand Using GIS Spatial Analysis

Urban density is one of the most important factors for the assessment of travel demand. It not only reflects the intensity of travel demand but also provides an effective method to quantify the influencing factors of travel demand. In this paper, two cities, Dalian and Shenyang, have been selected for case study. GIS spatial analysis technique has been introduced to analyze the average characteristics of general density, urban densities and their spatial difference. According to the characteristics of travel demand, urban densities have been divided into single density and composite density. Then spatial analysis method and spatial statistics method have been used to study the influence of urban densities on the occurrence, distance, time, and mode of travel. It has been concluded that different urban densities have certain effect on different travel demand, and within certain threshold, there is an obvious monotonous relationship between urban density and travel demand.

RISK OF ENERGY CONSTRAINED ACTIVITY-TRANSPORT SYSTEMS ( RECATS )

A long-term strategic planning tool called RECATS © is introduced. It explores the link between urban form and susceptibility to fuel shortages in order to quantify a risk factor at a queried year in the future for a given urban form and travel demand configuration. RECATS includes interactive models for global peak in oil production, development scenarios for alternative or bio-fuels, and fuel rationing or supply management. On the transport side, RECATS allows the user to specify the distance, mode, and frequency matrix which has been determined for a given urban form using standard transport modelling. RECATS was applied to a case study in which four different urban development forms for Christchurch, New Zealand were simulated. Results show that all urban forms would lose and/or change trips according to the land use-transport system configuration options, but the risk to activities would be very different for different future cities.

Dynamic Choice Model of Urban Commercial Activity Patterns of Vehicles and People

Intraurban commercial vehicle travel is a relatively underdeveloped aspect of urban travel demand modeling despite the large share of the weekday traffic stream represented by commercial movements. One problem is the proprietary nature of these data and the corresponding lack of behavioral understanding of how establishments schedule their trips. Even when such data have been made available, such as through establishment travel surveys, the large variation in firm size, commodities and services, and logistics practices makes it difficult to create a generalized decision framework. This work uses establishment survey data collected by the Ohio Department of Transportation to create an intraurban commercial vehicle model to be run in a disaggregate microsimulation environment and focuses on commercial movement patterns. The model generates entire daily patterns for workers who regularly travel as part of their jobs and creates tours through a dynamic choice process that incrementally builds tours, taking into consideration elapsed time and time of day in next-stop purpose and location choices. Activity durations are embedded in the utility equations of “stay” alternatives and provide internal consistency between the dimensions of activity purpose, duration, time of day, and location. Model formulation and estimation results are presented for the dynamic activity choice model component. The model system can reproduce observed commercial travel patterns found in the survey data and provide intuitively plausible interpretations for commercial travel behavior in the absence of more detailed knowledge of individual and firm operations.

Borrowing Residential Trip Generation Rates

Residential trip generation rates are a fundamental component of transportation planning. To investigate discrepancies in these rates, residential trip generation rates for nine suburban neighborhoods were computed using four different methods: (1) ground counts conducted at the neighborhoods, (2) household surveys distributed to the neighborhoods, (3) application of national trip generation rates published by the Institute of Transportation Engineers (ITE), and (4) rates derived from the trip generation component of regional urban travel demand models for the neighborhoods. Agencies generally use one of these rates, and by determining all four for the same set of neighborhoods in a controlled study, one can ascertain the extent to which these rates are likely transferable. Rates based on the first three methods were not significantly different. For developments composed solely of single-family detached homes, the average residential trip generation rate was 10.8 based on the site-specific ground counts, 9.2 based on site-specific household surveys, and 9.6 based on ITE's trip generation rates. However, rates based on the fourth method were significantly different, with a mean rate of 6.4. The greatest differences occurred when the long-range regional model used person trips that were converted to vehicle trips rather than estimating vehicle trips directly. Although the summary statistics presented in this paper will not surprise transportation planners, they illustrate two caveats for balancing data collection costs and need for site-specific information. First, a subtle change in how some rates are calculated limits their utility elsewhere. Second, even when equivalent methods for determining rates are used for similar neighborhoods, differences will occur because of the large and random variation inherent in trip generation. Borrowing rates may indeed be tolerable, but only if one gives the full range of rates possible from this probabilistic process rather than just the expected mean rate. Both caveats are treatable provided they are explicitly addressed through uncertainty analysis or adjustments for expected bias.

Is it time to use activity-based urban transport models? A discussion of planning needs and modelling possibilities

For some decades now, transport researchers have put considerable efforts into developing what is called activity-based approaches for modelling urban travel demand. The basic idea is that travel demand is derived from people’s desires to take part in different activities. In particular, the interrelationships among different activities with respect to temporal and spatial constraints are in focus. It means that such models treat the activities and the travelling of the households with respect to where and when the activities can be carried out and how they may be scheduled, given characteristics of the households and potential opportunities, the transport networks and various institutional constraints. We discuss what demands we see on future travel demand models, with a focus on urban analysis. This discussion is somewhat biased towards what role activity-based models could play in meeting these demands. We then review in some detail three prominent and distinctly different representatives of operational activity-based models to give an indication of what new modelling possibilities they offer. Theoretical appeal, empirical validity, usefulness for planning, need for data and easiness of implementation are discussed. In the final section we draw some conclusions about the prospects of these models and of their descendants.

Activity-based disaggregate travel demand model system with activity schedules

We present an integrated activity-based discrete choice model system of an individual’s activity and travel schedule, for forecasting urban passenger travel demand. A prototype demonstrates the system concept using a 1991 Boston travel survey and transportation system level of service data. The model system represents a person’s choice of activities and associated travel as an activity pattern overarching a set of tours. A tour is defined as the travel from home to one or more activity locations and back home again. The activity pattern consists of important decisions that provide overall structure for the day’s activities and travel. In the prototype the activity pattern includes (a) the primary – most important – activity of the day, with one alternative being to remain at home for all the day’s activities; (b) the type of tour for the primary activity, including the number, purpose and sequence of activity stops; and (c) the number and purpose of secondary – additional – tours. Tour models include the choice of time of day, destination and mode of travel, and are conditioned by the choice of activity pattern. The choice of activity pattern is influenced by the expected maximum utility derived from the available tour alternatives.

Handling Disaggregate Spatiotemporal Travel Data in GIS

Disaggregate travel data is not new to urban transportation planning studies, but it is infrequently handled in a GIS environment. With the evolution of urban travel demand models from aggregate mo...

Micm-Simulation Based Travel Demand Forecasting System for Urban Transportation Planning

This study proposes a new urban-travel demand forecasting system based on a micro-simulation approach as an alternative method to four-step estimation methods. This system comprises, 1) PCATS which simulates an individual's daily activity-travel pattern, 2) DEBNetS which simulates vehicle trips on urban road network, 3) a household simulator which generates future household attributes, and 4) a system to evaluate inter-zonal travel times and costs by public transit based on public transport networks and time tables. The system was applied to replicate the travel demand in a 1990 travel survey data set. A comparison between the observed and the forecast indicated that the system is capable of producing reasonable forecasts.

Analysis of travel mode and departure time choice for urban shopping trips

Travel mode and departure time choice of urban trips are important determinants of urban travel demand and the temporal distribution of this demand. While mode choice has received substantial attention in travel demand modeling, relatively little attention has been directed toward departure time choice. Further, the work trip has been the focus of most earlier mode choice research, and almost all earlier departure time choice research. The current paper examines the joint nature of mode and departure time choice for urban shopping trips. The model formulation in the paper adopts a nested structure with mode choice at the higher level of the hierarchy and departure time choice at the lower level. A multinomial logit (MNL) form is used for modeling mode choice and an ordered generalized extreme value (OGEV) form, which recognizes the natural temporal ordering of the departure time alternatives, is adopted for departure time choice. The proposed MNL-OGEV model is applied to data obtained from the 1990 San Francisco Bay area travel survey and is found to perform better than the MNL and nested logit models. In addition, the results indicate that the MNL and nested logit models lead to biased level-of-service estimates and to inappropriate policy evaluations of transportation control measures in the current empirical context.

Changes in travel time across modes and its impact on the demand for inter-urban rail travel

Analysis of the demand for inter-urban rail travel has received little attention in contrast to the large number of studies on urban travel demand. The studies on inter-urban rail demand usually emphasise the effects of monetary costs. Occasionally, changes in rail travel time are considered. However, the central aspect in our study is the impact of changes in road travel time on the demand for rail. This paper specifies and empirically estimates, an explanatory model to evaluate the impact of travel time changes on inter-urban rail demand. The change in rail passenger traffic between two periods on various routes is compared to the change in travel times on these routes. That is, it is a model estimated on the change in cross-section traffic volumes between the two periods. The empirical analysis confirms the explanatory power of changes in the intermodal structure of travel times, and shows the need to introduce the impact of these changes when studying the demand for inter-urban travel.

New approaches to transportation forecasting models

In 1992, the Federal Highway Administration awarded small research contracts to four teams of transportation researchers to design alternative approaches for improving the urban travel demand forecasting process. The purpose of these contracts was to enable each research team to explain how transportation planning models could and should be improved to meet the new forecasting requirements brought on by recent legislation, to address the impacts of new transportation technology, and to exploit the travel behavior theories and methodologies that have developed over the past two decades. This paper presents a summary and synthesis of the ideas which emerged from the four research reports. Its purpose is to identify common themes suggested by several of the research teams, to point out what appear to be critical elements missing from some approaches, and to combine the best aspects of the four approaches into a research plan for improving the current generation of travel demand models.

BOOK REVIEWS

Book reviewed in this article: New Directions in Spatial Econometrics, edited by Luc Anselin and Raymond J.G.M. Florax. Urban Travel Demand Modeling: From Individual Choices to General Equilibrium, by Norbert Oppenheim. The Economic Growth of Singapore: Trade and Development in the Twentieth Century, by W. G. Huff. Water in the Arab World: Perspectives and Prognoses, edited by Peter Rogers and Peter Lydon. China Deconstructs: Politics, Trade and Regionalism, edited by David S. G. Goodman and Gerald Segal. Technological Change, Economic Development and Space, edited by Cristoforo S. Bertuglia, Manfred M. Fischer, and Giorgio Preto. Local and Regional Development During the 1990s Transition in Eastern Europe, edited by Markku Tykkyliiinen. Territorial Competition in an Integrating Europe, edited by Paul Cheshire and Ian Gordon. State Per‐Capita Income Change Since 1950 Sharecropping's Collapse and Other Causes of Convergence, by Leonard F. Wheat and William H. Crown.

Nonresponse and Urban Travel Models

Considerable attention has been paid to the presence of nonresponse in large-scale travel surveys on the basis of which urban travel demand models are developed. It has been shown that the effect of nonresponse can be reduced by careful model building, with categorical trip generation models as an example. The same philosophy is extended to logit mode split models and exponential gravity models to show that the usual levels of nonresponse that one encounters in urban travel surveys have virtually no adverse effects on the parameter estimates of these models if the model has been specified correctly. Some simulation results are also presented to show the behavior of logit and exponential gravity model parameter estimates under conditions on nonresponse.

Nonresponse and Urban Travel Models

Considerable attention has been paid to the presence of nonresponse in large-scale travel surveys on the basis of which urban travel demand models are developed. It has been shown that the effect of nonresponse can be reduced by careful model building, with categorical trip generation models as an example. The same philosophy is extended to logit mode split models and exponential gravity models to show that the usual levels of nonresponse that one encounters in urban travel surveys have virtually no adverse effects on the parameter estimates of these models if the model has been specified correctly. Some simulation results are also presented to show the behavior of logit and exponential gravity model parameter estimates under conditions on nonresponse.